CV

Education

• Ph.D. in Computational Engineering, Durham University, 2023
  • Thesis: Automatic Extraction of Ordinary Differential Equations from Data: Sparse Regression Tools for System Identification
• M.S. in Data Science, Northwestern University, 2019
• B.A. in Statistics & Mathematics, Wittenberg University, 2016

Work experience

• April 2024 - Present: Postdoctoral Research Scientist
  • University of Hawaiʻi at Mānoa, Honolulu, HI
  • Developed Physics-Informed Neural Network framework for 4D-Variational Data Assimilation achieving 6.5-fold computational speedup over traditional numerical methods
  • Submitted manuscript to Journal of Advances in Modeling Earth Systems
  • Leading a Simons Foundation Targeted Award–funded workshop on machine learning methods for time-series data (2026)
  • Contributed to development of external research funding proposals focused on scientific modeling and data-driven methods
  • Supervisor: Dr. Brian Powell
• September 2017 - September 2019: Associate Business Analyst
  • Medline Industries, Northfield, IL
  • Reduced rebate inconsistencies by 30% through systematic analysis
  • Established relationships with stakeholders to resolve contract discrepancies
• January 2016 - May 2016: Data and Assessment Assistant
  • Springfield Promise Neighborhood, Springfield, OH
  • Developed logistic regression models and data visualizations for program evaluation

Skills

• Machine Learning
  • Physics-Informed Neural Networks (PINNs)
  • Automatic differentiation
  • Sparse regression
  • System identification
  • Bayesian hyperparameter optimization
• Data Assimilation & Modeling
  • 4D-Variational Data Assimilation (4D-Var)
  • Numerical methods
  • Dynamical systems
  • Coupled physical-biogeochemical modeling
• Programming & Scientific Computing
  • Python (PyTorch, NumPy, SciPy)
  • R, MATLAB, SQL
  • High-Performance Computing (HPC)
• Languages: Conversational Spanish

Publications

Automatically discovering ordinary differential equations from data with sparse regression

Egan, K., Li, W. & Carvalho, R. (2024). "Automatically discovering ordinary differential equations from data with sparse regression." Communications Physics. 7, 20.

Talks

Building a Physics-Informed Neural Network for NPZ Modeling

March 01, 2025

Workshop at CBIOMES Workshop on Photosynthesis and Primary Production, Virtual

Leveraging Physics-Informed Neural Networks for 4D-Variational Data Assimilation

January 01, 2025

Talk at CBIOMES Annual Meeting 2025, Virtual

Discovering Governing Equations from Data using Sparse Regression

August 01, 2021

Talk at Joint Statistical Meetings (JSM) & Symposium on Data Science & Statistics (SDSS), Virtual

Discovering Governing Equations from Data using Sparse Regression

July 01, 2021

Talk at SIAM Conference on Optimization (OP21) & Conference on Dynamical Systems (DS21), Virtual

Teaching Experience

Language and Culture Assistant

Math Workshop Tutor

Elementary School Teaching Assistant

Elementary Summer School Math Teacher

Software Contributions

• ARGOS (Automatic Regression for Governing Equations): Creator and maintainer of CRAN-published R package for system identification from time-series data
• PI-NPZ: Developer of PyTorch-based framework for 4D-Variational Data Assimilation using physics-informed neural networks

Grants, Workshops & Leadership Experience

• Lead Organizer & Presenter: CBIOMES Workshop on Machine Learning Methods for Time-Series Data (2026)
  Funded by a Simons Foundation Targeted Award
• Committee Member: Durham University Postgraduate Research Conference (2022)
• Chairman: Delta Tau Delta, Wittenberg University (2013–2016)